Semantic embedding: Traditionally ZSL methods report performance on both supervised attributes and unsupervised word2vec/glove as semantic embeddings. As manually labeled supervised attributes are hard to obtain, only small-scale datasets with these annotations are available [1]}, [2]}. ILSVRC-2017 detection dataset used in the current work is quite huge and does not provide attribute annotations. In this paper, we work on \(\ell _2\)  normalized 500 and 300 dimensional unsupervised word2vec [3]} and GloVe [4]} vector respectively to describe the classes. These word vectors are obtained by training on several billion words from Wikipedia dump corpus.
